Overview

Dataset statistics

Number of variables16
Number of observations430
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.9 KiB
Average record size in memory128.3 B

Variable types

Categorical7
Numeric9

Alerts

model has a high cardinality: 119 distinct values High cardinality
ROM is highly correlated with screen_size and 3 other fieldsHigh correlation
RAM is highly correlated with screen_size and 5 other fieldsHigh correlation
display_size is highly correlated with brand and 9 other fieldsHigh correlation
num_rear_camera is highly correlated with processor and 4 other fieldsHigh correlation
battery_capacity is highly correlated with brand and 8 other fieldsHigh correlation
ratings is highly correlated with brand and 6 other fieldsHigh correlation
num_of_ratings is highly correlated with salesHigh correlation
sales_price is highly correlated with brand and 8 other fieldsHigh correlation
sales is highly correlated with num_of_ratingsHigh correlation
processor is highly correlated with brand and 6 other fieldsHigh correlation
brand is highly correlated with processor and 5 other fieldsHigh correlation
base_color is highly correlated with num_front_camera and 1 other fieldsHigh correlation
screen_size is highly correlated with brand and 6 other fieldsHigh correlation
num_front_camera is highly correlated with base_color and 1 other fieldsHigh correlation
discount_percent is highly correlated with display_size and 1 other fieldsHigh correlation

Reproduction

Analysis started2022-09-12 15:48:40.910986
Analysis finished2022-09-12 15:49:09.750303
Duration28.84 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

brand
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Realme
138 
Samsung
119 
Xiaomi
61 
Apple
56 
Poco
56 

Length

Max length7
Median length6
Mean length5.886046512
Min length4

Characters and Unicode

Total characters2531
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApple
2nd rowApple
3rd rowApple
4th rowApple
5th rowApple

Common Values

ValueCountFrequency (%)
Realme138
32.1%
Samsung119
27.7%
Xiaomi61
14.2%
Apple56
13.0%
Poco56
13.0%

Length

2022-09-12T21:19:10.037581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T21:19:10.507165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
realme138
32.1%
samsung119
27.7%
xiaomi61
14.2%
apple56
13.0%
poco56
13.0%

Most occurring characters

ValueCountFrequency (%)
e332
13.1%
a318
12.6%
m318
12.6%
l194
 
7.7%
o173
 
6.8%
R138
 
5.5%
i122
 
4.8%
g119
 
4.7%
n119
 
4.7%
u119
 
4.7%
Other values (7)579
22.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2101
83.0%
Uppercase Letter430
 
17.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e332
15.8%
a318
15.1%
m318
15.1%
l194
9.2%
o173
8.2%
i122
 
5.8%
g119
 
5.7%
n119
 
5.7%
u119
 
5.7%
s119
 
5.7%
Other values (2)168
8.0%
Uppercase Letter
ValueCountFrequency (%)
R138
32.1%
S119
27.7%
X61
14.2%
A56
13.0%
P56
13.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2531
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e332
13.1%
a318
12.6%
m318
12.6%
l194
 
7.7%
o173
 
6.8%
R138
 
5.5%
i122
 
4.8%
g119
 
4.7%
n119
 
4.7%
u119
 
4.7%
Other values (7)579
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2531
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e332
13.1%
a318
12.6%
m318
12.6%
l194
 
7.7%
o173
 
6.8%
R138
 
5.5%
i122
 
4.8%
g119
 
4.7%
n119
 
4.7%
u119
 
4.7%
Other values (7)579
22.9%

model
Categorical

HIGH CARDINALITY

Distinct119
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
iPhone XR
 
18
iPhone 12
 
17
iPhone 12 Mini
 
16
GT Master Edition
 
9
X3
 
9
Other values (114)
361 

Length

Max length23
Median length15
Mean length8.651162791
Min length1

Characters and Unicode

Total characters3720
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)6.5%

Sample

1st rowiPhone SE
2nd rowiPhone 12 Mini
3rd rowiPhone SE
4th rowiPhone XR
5th rowiPhone 12

Common Values

ValueCountFrequency (%)
iPhone XR18
 
4.2%
iPhone 1217
 
4.0%
iPhone 12 Mini16
 
3.7%
GT Master Edition9
 
2.1%
X39
 
2.1%
M39
 
2.1%
M2 Pro9
 
2.1%
Galaxy A21s7
 
1.6%
Narzo 306
 
1.4%
Galaxy F626
 
1.4%
Other values (109)324
75.3%

Length

2022-09-12T21:19:10.790247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
galaxy117
 
12.6%
pro71
 
7.7%
iphone56
 
6.0%
5g48
 
5.2%
redmi41
 
4.4%
1233
 
3.6%
narzo31
 
3.3%
note26
 
2.8%
x321
 
2.3%
mi20
 
2.2%
Other values (99)464
50.0%

Most occurring characters

ValueCountFrequency (%)
498
 
13.4%
a288
 
7.7%
o212
 
5.7%
i187
 
5.0%
G185
 
5.0%
2170
 
4.6%
e150
 
4.0%
1138
 
3.7%
l133
 
3.6%
P129
 
3.5%
Other values (38)1630
43.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1727
46.4%
Uppercase Letter819
22.0%
Decimal Number674
 
18.1%
Space Separator498
 
13.4%
Dash Punctuation2
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G185
22.6%
P129
15.8%
M95
11.6%
A69
 
8.4%
R61
 
7.4%
N58
 
7.1%
X55
 
6.7%
F46
 
5.6%
C34
 
4.2%
T22
 
2.7%
Other values (9)65
 
7.9%
Lowercase Letter
ValueCountFrequency (%)
a288
16.7%
o212
12.3%
i187
10.8%
e150
8.7%
l133
7.7%
x123
7.1%
r121
7.0%
y117
6.8%
n83
 
4.8%
d60
 
3.5%
Other values (7)253
14.6%
Decimal Number
ValueCountFrequency (%)
2170
25.2%
1138
20.5%
397
14.4%
086
12.8%
580
11.9%
738
 
5.6%
625
 
3.7%
824
 
3.6%
48
 
1.2%
98
 
1.2%
Space Separator
ValueCountFrequency (%)
498
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2546
68.4%
Common1174
31.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a288
 
11.3%
o212
 
8.3%
i187
 
7.3%
G185
 
7.3%
e150
 
5.9%
l133
 
5.2%
P129
 
5.1%
x123
 
4.8%
r121
 
4.8%
y117
 
4.6%
Other values (26)901
35.4%
Common
ValueCountFrequency (%)
498
42.4%
2170
 
14.5%
1138
 
11.8%
397
 
8.3%
086
 
7.3%
580
 
6.8%
738
 
3.2%
625
 
2.1%
824
 
2.0%
48
 
0.7%
Other values (2)10
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
498
 
13.4%
a288
 
7.7%
o212
 
5.7%
i187
 
5.0%
G185
 
5.0%
2170
 
4.6%
e150
 
4.0%
1138
 
3.7%
l133
 
3.6%
P129
 
3.5%
Other values (38)1630
43.8%

base_color
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Blue
117 
Black
112 
White
44 
Silver
32 
Others
28 
Other values (7)
97 

Length

Max length6
Median length5
Mean length4.746511628
Min length3

Characters and Unicode

Total characters2041
Distinct characters27
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlack
2nd rowRed
3rd rowRed
4th rowOthers
5th rowRed

Common Values

ValueCountFrequency (%)
Blue117
27.2%
Black112
26.0%
White44
 
10.2%
Silver32
 
7.4%
Others28
 
6.5%
Green24
 
5.6%
Red21
 
4.9%
Gray20
 
4.7%
Yellow11
 
2.6%
Gold11
 
2.6%
Other values (2)10
 
2.3%

Length

2022-09-12T21:19:11.076455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
blue117
27.2%
black112
26.0%
white44
 
10.2%
silver32
 
7.4%
others28
 
6.5%
green24
 
5.6%
red21
 
4.9%
gray20
 
4.7%
yellow11
 
2.6%
gold11
 
2.6%
Other values (2)10
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e311
15.2%
l299
14.6%
B234
11.5%
a132
 
6.5%
u122
 
6.0%
r114
 
5.6%
k112
 
5.5%
c112
 
5.5%
i76
 
3.7%
h72
 
3.5%
Other values (17)457
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1611
78.9%
Uppercase Letter430
 
21.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e311
19.3%
l299
18.6%
a132
8.2%
u122
 
7.6%
r114
 
7.1%
k112
 
7.0%
c112
 
7.0%
i76
 
4.7%
h72
 
4.5%
t72
 
4.5%
Other values (9)189
11.7%
Uppercase Letter
ValueCountFrequency (%)
B234
54.4%
G55
 
12.8%
W44
 
10.2%
S32
 
7.4%
O28
 
6.5%
R21
 
4.9%
Y11
 
2.6%
P5
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin2041
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e311
15.2%
l299
14.6%
B234
11.5%
a132
 
6.5%
u122
 
6.0%
r114
 
5.6%
k112
 
5.5%
c112
 
5.5%
i76
 
3.7%
h72
 
3.5%
Other values (17)457
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2041
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e311
15.2%
l299
14.6%
B234
11.5%
a132
 
6.5%
u122
 
6.0%
r114
 
5.6%
k112
 
5.5%
c112
 
5.5%
i76
 
3.7%
h72
 
3.5%
Other values (17)457
22.4%

processor
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Qualcomm
168 
MediaTek
144 
Exynos
53 
Ceramic
33 
iOS
 
12
Other values (2)
20 

Length

Max length8
Median length8
Mean length7.418604651
Min length3

Characters and Unicode

Total characters3190
Distinct characters25
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWater
2nd rowCeramic
3rd rowWater
4th rowiOS
5th rowCeramic

Common Values

ValueCountFrequency (%)
Qualcomm168
39.1%
MediaTek144
33.5%
Exynos53
 
12.3%
Ceramic33
 
7.7%
iOS12
 
2.8%
Water11
 
2.6%
Others9
 
2.1%

Length

2022-09-12T21:19:11.328513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T21:19:11.613137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
qualcomm168
39.1%
mediatek144
33.5%
exynos53
 
12.3%
ceramic33
 
7.7%
ios12
 
2.8%
water11
 
2.6%
others9
 
2.1%

Most occurring characters

ValueCountFrequency (%)
m369
11.6%
a356
11.2%
e341
 
10.7%
o221
 
6.9%
c201
 
6.3%
i189
 
5.9%
Q168
 
5.3%
l168
 
5.3%
u168
 
5.3%
T144
 
4.5%
Other values (15)865
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2604
81.6%
Uppercase Letter586
 
18.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m369
14.2%
a356
13.7%
e341
13.1%
o221
8.5%
c201
7.7%
i189
7.3%
l168
6.5%
u168
6.5%
k144
 
5.5%
d144
 
5.5%
Other values (7)303
11.6%
Uppercase Letter
ValueCountFrequency (%)
Q168
28.7%
T144
24.6%
M144
24.6%
E53
 
9.0%
C33
 
5.6%
O21
 
3.6%
S12
 
2.0%
W11
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin3190
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m369
11.6%
a356
11.2%
e341
 
10.7%
o221
 
6.9%
c201
 
6.3%
i189
 
5.9%
Q168
 
5.3%
l168
 
5.3%
u168
 
5.3%
T144
 
4.5%
Other values (15)865
27.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m369
11.6%
a356
11.2%
e341
 
10.7%
o221
 
6.9%
c201
 
6.3%
i189
 
5.9%
Q168
 
5.3%
l168
 
5.3%
u168
 
5.3%
T144
 
4.5%
Other values (15)865
27.1%

screen_size
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Large
242 
Medium
146 
Small
34 
Very Small
 
4
Very Large
 
4

Length

Max length10
Median length5
Mean length5.43255814
Min length5

Characters and Unicode

Total characters2336
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery Small
2nd rowSmall
3rd rowVery Small
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Large242
56.3%
Medium146
34.0%
Small34
 
7.9%
Very Small4
 
0.9%
Very Large4
 
0.9%

Length

2022-09-12T21:19:11.873025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T21:19:12.121669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
large246
56.2%
medium146
33.3%
small38
 
8.7%
very8
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e400
17.1%
a284
12.2%
r254
10.9%
L246
10.5%
g246
10.5%
m184
7.9%
M146
 
6.2%
d146
 
6.2%
i146
 
6.2%
u146
 
6.2%
Other values (5)138
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1890
80.9%
Uppercase Letter438
 
18.8%
Space Separator8
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e400
21.2%
a284
15.0%
r254
13.4%
g246
13.0%
m184
9.7%
d146
 
7.7%
i146
 
7.7%
u146
 
7.7%
l76
 
4.0%
y8
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
L246
56.2%
M146
33.3%
S38
 
8.7%
V8
 
1.8%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2328
99.7%
Common8
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e400
17.2%
a284
12.2%
r254
10.9%
L246
10.6%
g246
10.6%
m184
7.9%
M146
 
6.3%
d146
 
6.3%
i146
 
6.3%
u146
 
6.3%
Other values (4)130
 
5.6%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e400
17.1%
a284
12.2%
r254
10.9%
L246
10.5%
g246
10.5%
m184
7.9%
M146
 
6.2%
d146
 
6.2%
i146
 
6.2%
u146
 
6.2%
Other values (5)138
 
5.9%

ROM
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.7488372
Minimum8
Maximum512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-09-12T21:19:12.342137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile32
Q164
median128
Q3128
95-th percentile256
Maximum512
Range504
Interquartile range (IQR)64

Descriptive statistics

Standard deviation63.16406421
Coefficient of variation (CV)0.5973026832
Kurtosis4.281647946
Mean105.7488372
Median Absolute Deviation (MAD)64
Skewness1.495005215
Sum45472
Variance3989.699008
MonotonicityNot monotonic
2022-09-12T21:19:12.568776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
128192
44.7%
64138
32.1%
3254
 
12.6%
25638
 
8.8%
165
 
1.2%
82
 
0.5%
5121
 
0.2%
ValueCountFrequency (%)
82
 
0.5%
165
 
1.2%
3254
 
12.6%
64138
32.1%
128192
44.7%
25638
 
8.8%
5121
 
0.2%
ValueCountFrequency (%)
5121
 
0.2%
25638
 
8.8%
128192
44.7%
64138
32.1%
3254
 
12.6%
165
 
1.2%
82
 
0.5%

RAM
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.320930233
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-09-12T21:19:12.789622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median4
Q36
95-th percentile8
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.182635235
Coefficient of variation (CV)0.4101980555
Kurtosis0.503140175
Mean5.320930233
Median Absolute Deviation (MAD)2
Skewness0.7468856991
Sum2288
Variance4.763896569
MonotonicityNot monotonic
2022-09-12T21:19:13.004077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4133
30.9%
6114
26.5%
888
20.5%
360
14.0%
221
 
4.9%
1212
 
2.8%
12
 
0.5%
ValueCountFrequency (%)
12
 
0.5%
221
 
4.9%
360
14.0%
4133
30.9%
6114
26.5%
888
20.5%
1212
 
2.8%
ValueCountFrequency (%)
1212
 
2.8%
888
20.5%
6114
26.5%
4133
30.9%
360
14.0%
221
 
4.9%
12
 
0.5%

display_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.369767442
Minimum4.7
Maximum7.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-09-12T21:19:13.245939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.7
5-th percentile5.445
Q16.3
median6.5
Q36.5
95-th percentile6.7
Maximum7.6
Range2.9
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.3695488863
Coefficient of variation (CV)0.05801607196
Kurtosis5.152325006
Mean6.369767442
Median Absolute Deviation (MAD)0.1
Skewness-1.553611897
Sum2739
Variance0.1365663794
MonotonicityNot monotonic
2022-09-12T21:19:13.509876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6.5164
38.1%
6.464
 
14.9%
6.762
 
14.4%
6.143
 
10.0%
6.322
 
5.1%
5.416
 
3.7%
6.614
 
3.3%
6.212
 
2.8%
5.86
 
1.4%
5.56
 
1.4%
Other values (7)21
 
4.9%
ValueCountFrequency (%)
4.74
 
0.9%
5.22
 
0.5%
5.416
 
3.7%
5.56
 
1.4%
5.61
 
0.2%
5.73
 
0.7%
5.86
 
1.4%
65
 
1.2%
6.143
10.0%
6.212
 
2.8%
ValueCountFrequency (%)
7.64
 
0.9%
6.92
 
0.5%
6.762
 
14.4%
6.614
 
3.3%
6.5164
38.1%
6.464
 
14.9%
6.322
 
5.1%
6.212
 
2.8%
6.143
 
10.0%
65
 
1.2%

num_rear_camera
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
3
157 
4
136 
2
97 
1
40 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters430
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
3157
36.5%
4136
31.6%
297
22.6%
140
 
9.3%

Length

2022-09-12T21:19:13.745715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T21:19:13.985375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3157
36.5%
4136
31.6%
297
22.6%
140
 
9.3%

Most occurring characters

ValueCountFrequency (%)
3157
36.5%
4136
31.6%
297
22.6%
140
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3157
36.5%
4136
31.6%
297
22.6%
140
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3157
36.5%
4136
31.6%
297
22.6%
140
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3157
36.5%
4136
31.6%
297
22.6%
140
 
9.3%

num_front_camera
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
1
413 
2
 
15
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters430
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1413
96.0%
215
 
3.5%
32
 
0.5%

Length

2022-09-12T21:19:14.206457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-12T21:19:14.446004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1413
96.0%
215
 
3.5%
32
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1413
96.0%
215
 
3.5%
32
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1413
96.0%
215
 
3.5%
32
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1413
96.0%
215
 
3.5%
32
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1413
96.0%
215
 
3.5%
32
 
0.5%

battery_capacity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4529.397674
Minimum1800
Maximum7000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-09-12T21:19:14.684023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1800
5-th percentile2815
Q14000
median4500
Q35000
95-th percentile6000
Maximum7000
Range5200
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation986.9072515
Coefficient of variation (CV)0.2178892918
Kurtosis0.05662835901
Mean4529.397674
Median Absolute Deviation (MAD)500
Skewness-0.2838950755
Sum1947641
Variance973985.9231
MonotonicityNot monotonic
2022-09-12T21:19:14.932305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5000129
30.0%
600054
12.6%
400049
 
11.4%
450042
 
9.8%
281533
 
7.7%
294218
 
4.2%
430016
 
3.7%
42009
 
2.1%
33008
 
1.9%
70006
 
1.4%
Other values (20)66
15.3%
ValueCountFrequency (%)
18005
 
1.2%
26002
 
0.5%
281533
7.7%
294218
 
4.2%
30002
 
0.5%
30802
 
0.5%
33008
 
1.9%
34002
 
0.5%
37001
 
0.2%
400049
11.4%
ValueCountFrequency (%)
70006
 
1.4%
600054
12.6%
51606
 
1.4%
50656
 
1.4%
50204
 
0.9%
5000129
30.0%
48202
 
0.5%
48001
 
0.2%
47801
 
0.2%
45203
 
0.7%

ratings
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.339302326
Minimum3
Maximum4.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-09-12T21:19:15.181901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.1
Q14.3
median4.3
Q34.4
95-th percentile4.6
Maximum4.6
Range1.6
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1514944259
Coefficient of variation (CV)0.03491216202
Kurtosis14.04302502
Mean4.339302326
Median Absolute Deviation (MAD)0.1
Skewness-1.73239576
Sum1865.9
Variance0.02295056107
MonotonicityNot monotonic
2022-09-12T21:19:15.668064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4.3181
42.1%
4.479
18.4%
4.555
 
12.8%
4.255
 
12.8%
4.637
 
8.6%
410
 
2.3%
4.18
 
1.9%
3.93
 
0.7%
31
 
0.2%
3.81
 
0.2%
ValueCountFrequency (%)
31
 
0.2%
3.81
 
0.2%
3.93
 
0.7%
410
 
2.3%
4.18
 
1.9%
4.255
 
12.8%
4.3181
42.1%
4.479
18.4%
4.555
 
12.8%
4.637
 
8.6%
ValueCountFrequency (%)
4.637
 
8.6%
4.555
 
12.8%
4.479
18.4%
4.3181
42.1%
4.255
 
12.8%
4.18
 
1.9%
410
 
2.3%
3.93
 
0.7%
3.81
 
0.2%
31
 
0.2%

num_of_ratings
Real number (ℝ≥0)

HIGH CORRELATION

Distinct175
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23567.94419
Minimum4
Maximum642373
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-09-12T21:19:15.946584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile23
Q1745
median5197.5
Q321089.25
95-th percentile123155.45
Maximum642373
Range642369
Interquartile range (IQR)20344.25

Descriptive statistics

Standard deviation56096.27778
Coefficient of variation (CV)2.380193934
Kurtosis47.93020113
Mean23567.94419
Median Absolute Deviation (MAD)4964.5
Skewness5.850072852
Sum10134216
Variance3146792381
MonotonicityNot monotonic
2022-09-12T21:19:16.225577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
536618
 
4.2%
74517
 
4.0%
24416
 
3.7%
76
 
1.4%
618126
 
1.4%
1056
 
1.4%
150166
 
1.4%
398816
 
1.4%
333646
 
1.4%
265
 
1.2%
Other values (165)338
78.6%
ValueCountFrequency (%)
43
0.7%
62
 
0.5%
76
1.4%
84
0.9%
101
 
0.2%
162
 
0.5%
193
0.7%
233
0.7%
265
1.2%
351
 
0.2%
ValueCountFrequency (%)
6423731
 
0.2%
4709051
 
0.2%
3570641
 
0.2%
2670281
 
0.2%
2269963
0.7%
2236722
0.5%
1552422
0.5%
1411772
0.5%
1296612
0.5%
1250163
0.7%

sales_price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct141
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25433.23488
Minimum5742
Maximum157999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-09-12T21:19:16.505631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5742
5-th percentile8287.45
Q111999
median16989.5
Q328999
95-th percentile72149
Maximum157999
Range152257
Interquartile range (IQR)17000

Descriptive statistics

Standard deviation22471.92659
Coefficient of variation (CV)0.8835654092
Kurtosis8.980504019
Mean25433.23488
Median Absolute Deviation (MAD)6490.5
Skewness2.595227695
Sum10936291
Variance504987484.6
MonotonicityNot monotonic
2022-09-12T21:19:16.792295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1499922
 
5.1%
1599914
 
3.3%
899912
 
2.8%
999912
 
2.8%
1699911
 
2.6%
1149911
 
2.6%
1049910
 
2.3%
429999
 
2.1%
479999
 
2.1%
219999
 
2.1%
Other values (131)311
72.3%
ValueCountFrequency (%)
57421
 
0.2%
64992
 
0.5%
68901
 
0.2%
69991
 
0.2%
72992
 
0.5%
74995
1.2%
79901
 
0.2%
79994
0.9%
80831
 
0.2%
81901
 
0.2%
ValueCountFrequency (%)
1579991
 
0.2%
1499993
0.7%
919992
 
0.5%
889992
 
0.5%
849992
 
0.5%
791496
1.4%
779992
 
0.5%
739991
 
0.2%
721496
1.4%
719991
 
0.2%

discount_percent
Real number (ℝ≥0)

HIGH CORRELATION

Distinct33
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.108
Minimum0.01
Maximum0.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-09-12T21:19:17.054114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.06
median0.09
Q30.16
95-th percentile0.24
Maximum0.44
Range0.43
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.07343201667
Coefficient of variation (CV)0.6799260803
Kurtosis2.269150088
Mean0.108
Median Absolute Deviation (MAD)0.04
Skewness1.301589636
Sum46.44
Variance0.005392261072
MonotonicityNot monotonic
2022-09-12T21:19:17.247947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.0944
 
10.2%
0.0838
 
8.8%
0.0432
 
7.4%
0.0729
 
6.7%
0.127
 
6.3%
0.0625
 
5.8%
0.0223
 
5.3%
0.0523
 
5.3%
0.222
 
5.1%
0.1621
 
4.9%
Other values (23)146
34.0%
ValueCountFrequency (%)
0.0111
 
2.6%
0.0223
5.3%
0.0317
 
4.0%
0.0432
7.4%
0.0523
5.3%
0.0625
5.8%
0.0729
6.7%
0.0838
8.8%
0.0944
10.2%
0.127
6.3%
ValueCountFrequency (%)
0.441
 
0.2%
0.431
 
0.2%
0.392
0.5%
0.361
 
0.2%
0.312
0.5%
0.34
0.9%
0.293
0.7%
0.283
0.7%
0.253
0.7%
0.243
0.7%

sales
Real number (ℝ≥0)

HIGH CORRELATION

Distinct216
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.75232558
Minimum0
Maximum550.19
Zeros3
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2022-09-12T21:19:17.407320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.079
Q11.64
median9.655
Q329.7175
95-th percentile130.29
Maximum550.19
Range550.19
Interquartile range (IQR)28.0775

Descriptive statistics

Standard deviation58.39958786
Coefficient of variation (CV)1.962857918
Kurtosis31.58655786
Mean29.75232558
Median Absolute Deviation (MAD)9.035
Skewness4.789041038
Sum12793.5
Variance3410.511862
MonotonicityNot monotonic
2022-09-12T21:19:17.562752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.079
 
2.1%
25.769
 
2.1%
5.156
 
1.4%
5.96
 
1.4%
1.766
 
1.4%
13.815
 
1.2%
105
 
1.2%
4.785
 
1.2%
1.525
 
1.2%
1.395
 
1.2%
Other values (206)369
85.8%
ValueCountFrequency (%)
03
0.7%
0.013
0.7%
0.022
0.5%
0.033
0.7%
0.053
0.7%
0.064
0.9%
0.074
0.9%
0.092
0.5%
0.12
0.5%
0.111
 
0.2%
ValueCountFrequency (%)
550.191
0.2%
493.981
0.2%
427.221
0.2%
392.731
0.2%
231.791
0.2%
2041
0.2%
182.121
0.2%
175.041
0.2%
174.91
0.2%
173.751
0.2%

Interactions

2022-09-12T21:19:06.197196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:48.561788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:50.960199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:53.000840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:55.079876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:57.357654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:59.497983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:01.667952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:04.079148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:06.430372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:48.885404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:51.179389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:53.227818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:55.317358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:57.589032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:59.729164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:01.934082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:04.304916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:06.666994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:49.100321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:51.402123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:53.455028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:55.573011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:57.825158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:59.981271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:02.347830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:04.539383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:06.901873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:49.422582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:51.649056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:53.688828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:55.829047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:58.048250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:00.229758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:02.584693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:04.791127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:07.138731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:49.658563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:51.877446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:53.918102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:56.203677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:58.278697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:00.465034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:02.813364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:05.014548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:07.400062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:50.001011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:52.121956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:54.159488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:56.447362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:58.552545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:00.719021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:03.056925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:05.271861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:07.665327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:50.240878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:52.348470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:54.392128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:56.670764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:58.797434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:00.963310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:03.308477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:05.504148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:08.005032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:50.488914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:52.570680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:54.628824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:56.905682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:59.046823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:01.197563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:03.544903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:05.759810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:08.364976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:50.716990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:52.787782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:54.852145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:57.131946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:18:59.270097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:01.439296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:03.832333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-12T21:19:05.978307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-12T21:19:17.707355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-12T21:19:17.998470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-12T21:19:18.169314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-12T21:19:18.336867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-12T21:19:18.500429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-12T21:19:09.110213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-12T21:19:09.634335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

brandmodelbase_colorprocessorscreen_sizeROMRAMdisplay_sizenum_rear_cameranum_front_camerabattery_capacityratingsnum_of_ratingssales_pricediscount_percentsales
0AppleiPhone SEBlackWaterVery Small6424.71118004.538645329990.17127.52
1AppleiPhone 12 MiniRedCeramicSmall6445.42128154.5244571490.041.39
2AppleiPhone SERedWaterVery Small6424.71118004.538645329990.17127.52
3AppleiPhone XROthersiOSMedium6436.11129424.65366429990.1023.07
4AppleiPhone 12RedCeramicMedium12846.12128154.6745691490.025.15
5AppleiPhone 12BlueCeramicMedium6446.12128154.6745641490.024.78
6AppleiPhone 12WhiteCeramicMedium12846.12128154.6745691490.025.15
7AppleiPhone 12GreenCeramicMedium6446.12128154.6745641490.024.78
8AppleiPhone 12BlueCeramicMedium12846.12128154.6745691490.025.15
9AppleiPhone 12BlackCeramicMedium12846.12128154.6745691490.025.15

Last rows

brandmodelbase_colorprocessorscreen_sizeROMRAMdisplay_sizenum_rear_cameranum_front_camerabattery_capacityratingsnum_of_ratingssales_pricediscount_percentsales
420XiaomiMi 10iBlackQualcommLarge12886.74148204.3663242150.061.61
421XiaomiRedmi Note 9 ProBlueQualcommLarge12846.72150204.46106141990.038.67
422XiaomiRedmi Note 9 ProBlueQualcommLarge12866.74150204.3434149990.060.65
423XiaomiRedmi Y3RedQualcommMedium3236.32140004.4684482520.315.65
424XiaomiRedmi 5BlueQualcommSmall1625.71133004.3426768900.182.94
425XiaomiRedmi 6 ProBlackQualcommSmall3235.82140004.3187079990.301.50
426XiaomiRedmi 6 ProRedQualcommSmall6445.82140004.3178396990.281.73
427XiaomiMi 11 LiteOthersQualcommLarge12866.53142504.21554219990.123.42
428XiaomiRedmi 8A DualBlueQualcommMedium3236.22150004.2816182990.076.77
429XiaomiRedmi 6 ProBlueQualcommSmall3235.82140004.3187081900.361.53